Abstract: This work proposes new low rank approximation approaches with significant memory savings for large scale MR fingerprinting (MRF) problems.

We introduce a compressed MRF with randomized SVD method to significantly reduce the memory requirement for calculating a low rank approximation of large sized MRF dictionaries. We further relax this requirement by exploiting the structures of MRF dictionaries in the randomized SVD space and fitting them to low-degree polynomials to generate high resolution MRF parameter maps.

In vivo 1.5 and 3 Tesla brain scan data are used to validate the approaches. It is shown that T1, T2 and off-resonance maps are in good agreement with that of the standard MRF approach. Moreover, the memory savings is up to 1000 times for the MRF-FISP sequence and more than 15 times for the MRF-bSSFP sequence.

The proposed compressed MRF with randomized SVD and dictionary fitting methods are memory efficient low rank approximation methods, which can benefit the usage of MRF in clinical settings. They also have great potentials in large scale MRF problems, such as problems where multi-component chemical exchange effects are considered.